Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training

By Shangshu Qian, Hung Viet Pham, Thibaud Lutellier, Zeou Hu, Jungwon Kim, Lin Tan, Yaoliang Yu, Jiahao Chen, Sameena Shah. 2021

49 citations recorded by the source corpus.

Subfields: Evaluation & Benchmarks, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Evaluation & Benchmarks

1. Hong Chang
2. Daniel D'souza
3. Wei-Yin Ko
4. Karina Nguyen
5. Randall Balestriero
6. Reza Shokri
7. Martin Strobel
8. Sara Hooker

In Safety, Robustness & Interpretability

1. Martin Strobel
2. Hong Chang
3. Randall Balestriero
4. Reza Shokri
5. Sara Hooker
6. Prakhar Ganesh
7. Prateek Mittal
8. Liwei Song

Citations in the corpus

• On The Impact of Machine Learning Randomness on Group Fairness (2023, background): “3 High Variance in Fair Deep Learning There has been a growing awareness of high variance in fair deep learning, associated with non-determinism in model training or the underlying implementation [3, 21, 43, 48, 51], and the uncertainty of existing results in the literature.”
• FAIR-Ensemble: When Fairness Naturally Emerges From Deep Ensembling (2023, background): “Most relevant to our work is (Qian et al., 2021; Zhuang et al., 2022; Madhyastha & Jain, 2019; Summers & Dinneen, 2021) that evaluates how stochasticity in training impacts fairness in DNN Systems.”